Relationship between social development indicators and mortality due to Diabetes Mellitus in Brazil: a space-time analysis

Objective: to identify the space-time pattern of mortality due to Diabetes Mellitus in Brazil, as well as its relationship with social development indicators. Method: an ecological and time series nationwide study based on secondary data from the Unified Health System Informatics Department, with space-time analysis and inclusion of indicators in non-spatial and spatial regression models. The following was performed: overall mortality rate calculation; characterization of the sociodemographic and regional profiles of the death cases by means of descriptive and time analysis; and elaboration of thematic maps. Results: a total of 601,521 deaths related to Diabetes Mellitus were recorded in Brazil, representing a mean mortality rate of 29.5/100,000 inhabitants. The states of Rio Grande do Norte, Paraíba, Pernambuco, Alagoas and Sergipe, Rio de Janeiro, Paraná and Rio Grande do Sul presented high-high clusters. By using regression models, it was verified that the Gini index (β=11.7) and the Family Health Strategy coverage (β=3.9) were the indicators that most influenced mortality due to Diabetes Mellitus in Brazil. Conclusion: in Brazil, mortality due to Diabetes presents an overall increasing trend, revealing itself as strongly associated with places that have worse social indicators.


Introduction
Diabetes Mellitus (DM) and its complications constitute a relevant and growing global public health problem, representing one of the leading causes of premature deaths in individuals over 60 years of age. Globally, recent data from the International Diabetes Federation show a total of 6.7 million deaths caused by the disease in 2021, reaching the milestone of one death every five seconds (1) .
Meanwhile, Brazil is the country with the highest number of people with Diabetes in Latin America, ranking fifth in the world. The mortality rates due to the disease have nearly doubled in the last decades, rising from 16.3 deaths per 100,000 inhabitants in 1996 to 29 deaths per 100,000 inhabitants in 2019, accounting for 30.1% of all premature deaths (2) .

The prevalence of Diabetes varies in the different
Brazilian regions, with rates of 6.8% in the North, 8.7% in the Northeast, 10.5% in the Southeast, 8.5% in the South, and 10.3% in the Midwest. In this sense, the proportion of underreporting in the national scenario stands out, estimated at 72.8% in the North region (3) .
In light of this panorama, it is recognized that economic and social conditions decisively influence the quality of life and health of populations, while reflecting indicators such as wealth distribution, housing conditions, schooling and access to health services. Within the DM context, these indicators translate into lower knowledge about the disease, poorer clinical management quality and a higher risk of unfavorable outcomes such as acute and chronic complications or death (4) .
In this perspective, the literature reiterates the association of social determinants with hospitalizations (5) and complications (6) due to DM. However, there is lack of research studies evaluating these aspects as predictors of death due to DM. Therefore, it is reinforced that this is the first national study that strives to analyze the relationship between social development indicators and the outcome of death related to DM, also considering the effect of geographical space.
In addition, the study proposed is unique in its approach to guiding, planning and implementing Nursing care, given the understanding of the sociodemographic profile and behavioral trends of the outcome of death In this regard, the objective of this study was to identify the space-time pattern of mortality due to Diabetes Mellitus in Brazil and its relationship with social development indicators.

Method Study design
An ecological study guided by the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) tool.

Study locus
The study was conducted throughout the entire Brazilian territory (8,510,345

Period
The study considered the available data from January 2010 to December 2020, while data collection and analysis were conducted from June 2021 to March 2022.

Population definition
Deaths reported from January 2010 to December 2020 in individuals above ten years of age were included, related to the following categories from the Tenth For this study, age groups starting from ten years old were considered, given the insignificant contribution of individuals under the age of ten to the outcome of death due to Diabetes Mellitus, which is reflected in the very low number of notifications in the national context, a fact supported by the literature (7) . www.eerp.usp.br/rlae 3 Garces TS, Damasceno LLV, Sousa GJB, Cestari VRF, Pereira MLD, Moreira TMM.

Data collection
The secondary data were obtained from the

Study variables
Regarding the predictive variables related to the health context and agreements, the following ones were selected: Selection of these variables was motivated by previous studies that performed space-time analyses (5)(6)(7)(8) , reflecting characteristics related to demographics, schooling, income, employment and housing. It is also noted that the most recent data available in these databases were used. For instance, the primary care coverage databases are regularly updated on a monthly basis, whereas those related to the demographic census were last updated in 2010. However, these variables were still considered because they reflect the social, economic and health situation of the Brazilian population.

Data analysis
Initially, the unadjusted mortality rate was calculated and Average Annual Percentage Change (AAPC) with their 95% confidence intervals (CIs) (9) .
The analysis using joinpoints allows for the inclusion of multiple line segments, demonstrating changes in linear trends during the study period. It is noted that the connection between two straight line segments or two trends occurs at a joinpoint, which represents an inflection point. A significance level of p<0.05 was considered when the model showed statistical significance. It is important to note that positive and significant results indicate am increasing trend, while negative and significant results indicate a decreasing trend and non-significant results indicate a stationary pattern (9) .
For the spatial analysis, the mean rate was calculated by dividing the mean number of cases during the 11-year study period in each municipality by the estimated population in the middle of the period (2015). Finally, the result was multiplied by 100,000 inhabitants. Thematic maps were generated based on the mean unadjusted mortality rate during the entire study period. The rates were smoothed using the local empirical Bayesian method in order to reduce their instability. This method considers not only the value of the municipality but also weights it in relation to neighboring municipalities through a spatial proximity matrix. For calculation of the matrix, the contiguity criterion was used, assigning a value of 1 to municipalities that had neighboring municipalities and 0 to non-neighboring municipalities (10) .
The spatial autocorrelation of deaths was calculated using the Global Moran Index (GMI) based on the unadjusted indicators. The method identifies spatial autocorrelation and ranges from -1 to +1, where values close to zero indicate absence of spatial dependence. Once the GMI is found to be significant, the Local Indicators of Spatial Association (LISA) are calculated, evaluated by means of the Local Moran Index (LMI), enabling the identification of local patterns of deaths (11) .
In turn, LMI generates Moran scatterplots, which consist of four quadrants: high-high (cities with high rates surrounded by others with high rates), low-low (cities with low rates surrounded by others with low rates), high-low (cities with high rates surrounded by others with low rates), and low-high (cities with low rates surrounded by others with high rates). The "high-high" and "low-low" categories represent agreement areas, whereas "high-low" and "low-high" indicate epidemiological transition areas (12) .
In addition, in order to test the relationship of each To select the model that best estimated this relationship, the GWR results were compared to OLS using two estimates: the R² value and the Akaike Information Criterion (AIC). The AIC value expresses the amount of information lost when the data are approximated with a model. Therefore, the best model will be the one that is closest to the probabilistic process that generated the data; in other words, the one with the lowest AIC value, as it approximates the data with the lowest information loss.
Furthermore, R² represents a goodness of fit measure.
Its value varies from 0.0 to 1.0, where higher values are preferable. It can be interpreted as the variance proportion in the dependent variable accounted for by the regression model (13) . In this sense, after comparing the spatial model with the non-spatial one, the one that had the highest R² value and the lowest AIC was chosen for analysis.
Microsoft Excel was used for calculating the mortality rates and producing the line graphs for descriptive statistics. Calculation of the local empirical Bayesian rate, GMI and LMI was performed using the TerraView 4.

Ethical aspects
As the study uses free-access secondary data

Results
Between 2010 and 2020 there were 601,521 deaths related to DM in Brazil. This number represents a mean mortality rate of 29.8 per 100,000 inhabitants.
In Table 1, it can be seen that the mortality rate among females (32.1 per 100,000 inhabitants) is higher than both the rate among males (26.9 per 100,000 inhabitants) and the overall rate observed in the country (29.8 per 100,000 inhabitants). In addition to that, the mortality rate increases with age, reaching its peak in the age group of 80 years old or more (508.46 per 100,000 inhabitants). Among the racial groups, the mortality rate is higher in individuals of mixed race (36.16 per 100,000 inhabitants) and, among the schooling levels, it is higher in people with up to three years of studies (59.53 per 100,000 inhabitants). The AAPC corresponding to the period was considered for the analysis of the mortality trends. In this way, it was possible to observe significant heterogeneity in the Brazilian territory, even in states from the same region.
In the country as a whole, 11 states presented a significant increase in the mortality rate due to DM, whereas two states and DF showed a reduction. The remaining 13 districts did not present statistical significance, thus interpreting them as with a stable trend ( Table 2).
In addition, it is worth noting that due to the demographic, social and economic heterogeneity found in Brazil, different patterns of mortality due to DM can be observed in various regions (3) , with the Northeast and South being marked by high-high values and the formation of high-risk clusters (17) . The prevalence of high mortality rates in these regions is influenced by factors caused by DM (21) .
As an example, a statistically significant association was found between the illiteracy rate among adults over Brazilian regions (3,18) . This finding indicates that increasing the employment and wage-earning opportunities can reduce the DM risk and, consequently, the number of deaths due to the disease. which interferes in the unequal distribution of power, prestige and resources (24) .

Additionally, health indicators in individuals with
It is a fact that social vulnerability contributes to increased exposure and susceptibility to health risks and mortality, resulting from individual factors and collective conditions, as well as the availability of resources to address them. This is reflected in higher mortality due to DM in census tracts characterized by medium and high social vulnerability (21) . In this context, the importance of governmental transfers for the social protection of subjects in situations of vulnerability gains relevance.
Although the Brazilian health system was conceived to be universal, comprehensive and equitable for the entire population, its mixed institutional design with a significant percentage of health services provided by the private sector, seems to feed inequalities in access and unfavorable health outcomes, added to the inequality in regional income distribution (25) , representing an additional difficulty for the population with CNCDs living in poorer areas, as they face insufficient provision of services from the SUS.
The problem also seems to be related to access to health-related information. Health is a right for every Brazilian, and access to health information through education is a fundamental strategy for empowering individuals and communities, based on raising awareness about their health conditions and their role in this process (26) .
In this context, the contribution of Nursing can be enhanced through the use of methods that promote effective and meaningful learning, with planned and adapted educational interventions for each learning level (27) , such as discussion groups, the use of playful interventions, welcoming in the waiting room, campaigns and case studies, among others. In these opportunities, it is important to prioritize information about DM and healthy lifestyles (28) , in order to increase knowledge and Garces TS, Damasceno LLV, Sousa GJB, Cestari VRF, Pereira MLD, Moreira TMM.
skills for daily care, prevention of complications and unfavorable outcomes such as death.
It is noted that, in order to understand health outcomes, it is important to consider economic and social relationships, as well as their performance in geographic spaces and, consequently, their influence on the health-disease process of populations. In light of this, it is suggested to develop more in-depth studies involving population samples, field research studies and interventions, aiming to propose and implement measures targeted at changing this scenario.
It is reiterated that populations are subjected to cultural, demographic and socioeconomic heterogeneity, which leads to disparities in the quality of care provided, as well as in diagnostic capacity and in quality of the information provided; in addition, political and economic development also influences performance in health, extending to components such as education and income (18) .
A wide scope for overall improvement in the care of patients with DM can be seen ahead, with an urgent need to include the topic in the intra-and inter-sectoral public health policies developed in the country, especially in terms of reducing social disparities, expanding access to health and implementing policies for promotion, education, prevention and surveillance regarding diabetes (23-29) .
The study presents some limitations, namely: the

Conclusion
The space-time pattern of mortality due to Diabetes Mellitus in Brazil shows an overall increasing trend, where spatial clusters can be seen particularly in the Northeast and South regions, also revealing itself as associated with areas characterized by worse sociodemographic indicators such as income distribution, housing conditions, schooling and access to health.